论文标题
深度学习，以分类和表征海事环境中的大气管道
Deep Learning for Classifying and Characterizing Atmospheric Ducting within the Maritime Setting
论文作者
论文摘要
海洋大气边界层内折射率的实时表征可以提供有价值的信息，这些信息可能有可能用于减轻大气管道对雷达性能的影响。许多管道表征模型成功地从与给定类型的管道相关的特定折射率概况预测参数。但是，分类然后随后表征的能力，各种管道类型是朝着更全面的预测模型迈出的重要一步。我们使用深度学习介绍了两步方法，以区分蒸发管道条件下收集的稀疏采样的传播因子测量值，这些测量是在基于表面的管道条件下收集的，以便随后根据该差异估算适当的折射参数。我们表明，这种方法不仅是准确的，而且是有效的。因此为实时应用提供了合适的方法。
Real-time characterization of refractivity within the marine atmospheric boundary layer can provide valuable information that can potentially be used to mitigate the effects of atmospheric ducting on radar performance. Many duct characterization models are successful at predicting parameters from a specific refractivity profile associated with a given type of duct; however, the ability to classify, and then subsequently characterize, various duct types is an important step towards a more comprehensive prediction model. We introduce a two-step approach using deep learning to differentiate sparsely sampled propagation factor measurements collected under evaporation ducting conditions with those collected under surface-based duct conditions in order to subsequently estimate the appropriate refractivity parameters based on that differentiation. We show that this approach is not only accurate, but also efficient; thus providing a suitable method for real-time applications.
